Amid sociopolitical precarity and denials of social and institutional care, trans people find ways to support each other – named trans care. Alongside nascent HCI work characterising trans care, we conduct focus groups and supplementary participatory speculative design with 17 trans adults in the UK to investigate how trans people use social media to support trans care, and the sociotechnical conditions shaping such care. Based on reflexive thematic analysis and member reflections, we describe how social media support essential community building, identity work, responses to healthcare and legal barriers, everyday care, and resistance work, highlighting sociotechnical tensions animating this care. Simultaneously, we find that algorithmic curation and moderation systematically undermines trans care and exposes trans people to pervasive, yet unpredictable harm. We argue that social media are essential technologies for trans care, yet commercial platforms simultaneously act as precarisation machines, and discuss design orientations for supporting trans people and/alongside trans care.

Large Language Models (LLMs) offer potential benefits for increasing access to digital well-being support, yet their application raises important questions about risks and responsible implementation. This paper examines a critical, often overlooked, dimension of LLM safety: cultural and social alignment in underrepresented contexts. We investigate how LLM-mediated emotional support can be adapted for a specific cultural setting, using Saudi Arabia as a case study. We present CSESC, a Culturally Sensitive Emotional Support Chatbot, developed as a technology probe to explore user perceptions of culturally sensitive responses. Our adaptation process was grounded in emotional support frameworks and guided by multicultural guidelines and local expertise. User evaluations demonstrate that cultural alignment enhances users’ sense of relatedness, while also surfacing tensions between empathy and sociocultural norms. We discuss the notion of “minimum cultural alignment,” contributing to HCI literature on culturally responsive LLM design and broadening the understanding of LLM safety.

Young Saudi women (YSW) face increasing mental health challenges, often hidden behind social barriers and stigma. While digital support has been investigated in Western contexts to offer discreet alternatives, its application in Saudi Arabia remains understudied, particularly in addressing unique cultural needs. To address this gap, we conducted remote interviews with 20 YSW to explore their lived experiences and challenges with existing Arabic apps, followed by remote co-design workshops with 38 YSW to explore design preferences. Using thematic analysis, our findings reveal that many apps rely on surface-level translation and fail to align with users’ cultural values, limiting their adoption. Concerns about incomplete anonymity, fear of being discovered, and a lack of guidance further challenged their use. Participants also framed online privacy as a collective moral value, rather than an individual concern. We discuss these sociocultural sensitivities and propose design considerations for future design targeting Saudi and broader Arab audiences.

The experience of leaving North Korea and navigating life as an undocumented person in China presents significant challenges for those seeking refuge in South Korea. This paper presents findings from an eight-month longitudinal ethnographic study with five newly arrived North Korean defector women in their first year in South Korea. We closely examine how digital technologies are intertwined with their everyday adaptation: how newcomers engage with digital systems, what frictions emerge, and how past experiences of living invisibly shape these interactions. This study contributes to the HCI community by (1) offering an empirically grounded, underrepresented account of early-stage digital resettlement among North Korean defectors; (2) conceptualizing first-time digital identity as a site of acute vulnerability shaped by past experiences and long histories of marginalization; and (3) proposing temporal, context-sensitive, self-reliance–oriented approaches to digital adaptation that supports vulnerable newcomers’ sociotechnical transition into a new society.

North Korean defector mothers raising children in South Korea face challenges in balancing disclosure of their past with protecting their children from discrimination and stigma. Drawing on interviews with 22 North Korean defector mothers, we examine how children’s exposure to polarized media on North Korea acts as an unexpected, external force that shapes prejudice toward mothers’ backgrounds. This sociotechnical influence, combined with a lack of meaningful storytelling resources, complicates mothers’ disclosure decisions and constrains narrative agency over their own identities. We contribute to HCI by (1) advancing an empirically grounded understanding of how digital technologies mediate stigmatized identity disclosure in families, reshaping mother-child dynamics and mothers’ identity negotiations; (2) proposing design directions for storytelling technologies that support narrative agency, silence, and emotionally safe, anonymous sharing of disclosure strategies among mothers with stigmatized histories; (3) foregrounding the lived experiences and familial dynamics of a highly underrepresented population navigating digitally saturated ecosystems.

Decentralised social media (DSM) platforms such as Mastodon offer community-governed alternatives to corporate social networks but place substantial governance burdens on volunteer operators. As interest grows in applying artificial intelligence (AI) to support this work, little is known about whether DSM operators want AI, what roles they consider appropriate, and what governance boundaries they require. We conducted semi-structured interviews with 20 operators across Mastodon, Pixelfed, PeerTube, Lemmy, Pleroma, and Funkwhale, using generative feature probes and speculative scenarios to explore their perceptions of AI. Operators rejected AI as an autonomous actor, instead envisioning it as governance infrastructure that provides contextual intelligence, supports cross-instance coordination, and sustains community and moderator well-being. They also articulated strict boundaries rooted in DSM values, including human accountability, reversibility, transparency, community-centred configuration, and strong data-governance constraints. We contribute empirical insights and design implications for AI compatible with decentralised, federated social media.

As Human-Computer Interaction (HCI) researchers increasingly conduct studies involving populations with stigmatised identities, more researchers must focus on creating conversational spaces where participants feel comfortable discussing their experiences. This study unpacks how and why HCI researchers engage in self-disclosure to facilitate such spaces. We share eight autoethnographic personal stories of key moments when we disclosed or concealed personal information in our HCI studies with diverse participant groups (people with dementia, victims of online romance scams, children with autism, and North Korean defectors). Through analysing these stories, we found that these decisions were shaped by various goals: creating a welcoming environment, demonstrating credibility, establishing common ground, reducing stigma, and directing the interview’s focus. The acts of disclosure or concealment affected the interactions with participants, the collected data, and the well-being of the researcher and participant. We argue that greater awareness, reflection, guidance, and sharing are needed regarding self-disclosure in HCI research and offer a reflexive guide to help researchers prepare for and reflect on their self-disclosure practices. By making these practices visible and open to discussion, we aim to make researcher-participant interactions in sensitive settings more ethical, effective, and transparent. • HCI researchers dynamically disclose/conceal personal information with stigmatised groups. • Researchers differ in the who/what/when/why(not)/how of their self-disclosure. • HCI lacks awareness, guidelines, and transparency for researcher self-disclosure. • Reflexivity around self-disclosure is needed for ethical and effective research.

Is 'art' simply 'content' in the age of social media? What does art's creation, appreciation, and experience lose when art becomes content? This exploratory research employs a mixed methods approach encompassing interviews, co-design, and stakeholder reviews to critically examine the perceptions of artists, curators, and content creators regarding the distinction between 'art' and 'content.' Our participants reveal four characteristics essential for art experiences that we use to co-design new digital 'content' environments. The work concludes with content creators reviewing the characteristics and designs, offering their thoughts on the feasibility and potential future for 'art' on social media. We offer open-source access to all design and research materials. By exploring the divide between 'art' and 'content,' this research contributes insights and guidelines for developing alternative algorithmic experiences to support creativity on social media.

Social media has become a primary source of entertainment and education for children globally. While much attention has been given to children’s online well-being, a pressing concern often goes unnoticed: the pervasive data harvesting underlying social media and its manipulative impact on undermining children’s autonomy. In this paper, we present CHAITok, an Android mobile application designed to enhance children’s sense of autonomy over their data on social media. Through 27 user study sessions with 109 children aged 10–13, we offer insights into the current lack of data autonomy among children regarding their online information, and how we can foster children’s sense of data autonomy through a socio-technical journey. Our findings inspire design recommendations to respect children’s values, support children’s evolving autonomy, and design for children’s digital rights. We emphasize data autonomy as a fundamental right for children, call for further research, design innovation, and policy changes on this critical issue.

Mental health disorders are prevalent worldwide, yet they remain stigmatized, especially in the Middle East. While mHealth has the potential to circumvent traditional barriers, research on its application remains scarce in Arab countries. To address this gap, we conducted a mixed-methods study of mental health apps availability, adoption, and perceptions in the Kingdom of Saudi Arabia (KSA) where digital health transformation is rapidly progressing. We interviewed twelve psychiatrists and psychologists to elicit their views on local barriers and opportunities for digital mental health support. We further systematically reviewed the Saudi app market, analysing 110 Arabic mental health apps. Our findings indicate that whilst fear of stigma and cultural factors hindered help-seeking, the privacy and anonymity enabled by technology created new opportunities for accessing mental support in the KSA. We revealed tensions between experts’ professional and practical perspectives, explored technology-exacerbated challenges and provided considerations for improving Saudi digital mental healthcare experience.

Digital self-control tools (DSCTs) help people control their time and attention on digital devices, using interventions like distraction blocking or usage tracking. Most studies of DSCTs’ effectiveness have focused on whether a single intervention reduces time spent on a single device. In reality, people may require combinations of DSCTs to achieve more subjective goals across multiple devices. We studied how DSCTs can address individual needs of university students (n = 280), using a workshop where students reflect on their goals before exploring relevant tools. At 1-3 month follow-ups, 95% of respondents still used at least one type of DSCT, typically applied across multiple devices, and there was substantial variation in the tool combinations chosen. We observed a large increase in self-reported digital self-control, suggesting that providing a space to articulate goals and self-select appropriate DSCTs is a powerful way to support people who struggle to self-regulate digital device use.

In a world where digital technology is omnipresent, North Korea stands as an outlier, with most citizens uninformed about its existence. This study explores the experiences of North Korean defectors as they transition to a highly digitally connected society—South Korea. Through 21 semi-structured interviews, we initially investigate the critical needs and challenges they encounter during their transitions. Then, we examine the role of digital technology as they adapt to the highly connected digital environment of South Korea. Our findings highlight that social media serves as a double-edged sword, providing the freedom to construct a desired identity while accentuating the gap between their real and ideal selves. This empirical research offers insights into how an underrepresented population navigates the digital landscape during life transitions, shedding light on the drawbacks and ways to better address their needs.

Children today are deeply immersed in the online world, where their activities are routinely tracked, analysed, and monetised. This exposes them to various datafication risks, including harmful profiling, micro-targeting and behavioural engineering. Most existing measures focus on immediate online threats, rather than informing children about these implicit risks. In this paper, we present The KOALA Hero Toolkit, a hybrid toolkit designed to help children and parents jointly understand the datafication risks posed by their mobile apps. Through user studies involving 17 families we evaluate how the toolkit influenced families’ thought processes, perceptions and decision-making regarding mobile datafication risks. Our findings show that KOALA Hero supports families’ critical thinking and promotes family engagement. We identify future design recommendations for family support, featuring ideas such as integrating triggering moments and bonding moments in toolkit designs. This work provides timely inputs on global efforts aimed at addressing datafication risks and underscores the importance of strengthening legislative and policy enforcement of ethical data governance.

Decentralised social media platforms are increasingly being recognised as viable alternatives to their centralised counterparts. Among these, Mastodon stands out as a popular alternative, offering a citizen-powered option distinct from larger and centralised platforms like Twitter/X. However, the future path of Mastodon remains uncertain, particularly in terms of its challenges and the long-term viability of a more citizen-powered internet. In this paper, following a pre-study survey, we conducted semi-structured interviews with 16 Mastodon instance administrators, including those who host instances to support marginalised and stigmatised communities, to understand their motivations and lived experiences of running decentralised social media. Our research indicates that while decentralised social media offers significant potential in supporting the safety, identity and privacy needs of marginalised and stigmatised communities, they also face considerable challenges in content moderation, community building and governance. We emphasise the importance of considering the community's values and diversity when designing future support mechanisms.

Interfaces increasingly mimic human social behaviours. Beyond prototypical examples like chatbots, basic automated systems like app notifications or self-checkout machines likewise address or 'talk to' people in person-like ways. Whilst early evidence suggests social cues can enhance user experience, we lack a good understanding of when, and why, their use in interaction design may be inappropriate. We combined a qualitative survey (n=80) with experience sampling, interview, and workshop studies (n=11) to understand people's attitudes and preferences regarding how a range of automated systems talk to/at them. We thematically analysed examples of phrasings or conduct our participants disliked, their reasons, and how they would prefer to be treated instead. One category of inappropriate use we identified is when social design elements are used to manipulate user behaviour. We distinguish four such tactics: 'agents' playing on users' emotions (e.g., guilt-tripping, coaxing), being pushy, mothering users, or being passive-aggressive. Another category regards pragmatics: personal or situational factors that can make even a seemingly helpful or friendly message come across as rude, tactless, invasive, etc. These include contextual insensitivity (e.g., embarrassing users in public); expressing clearly false personalised care; or treating a user in ways they find misaligned with the system's role or the nature of their relationship. We discuss these inappropriate uses in terms of an emerging 'social' class of dark and anti-patterns. From participant suggestions, we offer recommendations for improving how interfaces treat people in interaction, including broader normative reflections on treating users respectfully.

Early parenting is one of the strongest predictors of child well-being. Online social communities have shown promise in supporting parents across a range of contexts. However, we only have a limited understanding of how posters and commenters interact within a forum, or how well commenter responses can support complex parenting questions, such as attempts to change a child's behaviour or to apply new parenting approaches. We start addressing this gap by combining an empirical analysis of 1 year of parent posts from an exemplar online forum (Mumsnet) with literature on parenting interventions from psychology. In particular, we examine the types of question parents of 2-5 year olds seek help for around their children's behaviour, and the challenges with the support that they do (or do not) receive from the Mumsnet community. Combining empirical and theory-driven insights, we outline an 'information-to-application' gap that conceptually underpins the difficulties observed, and suggest plausible research directions that could address such design problems.

In recent years, growing research has been made on supporting children to become more autonomous in the digital environment around them. However, there has been little consensus regarding the conceptualisation of digital autonomy for children in the HCI community and how best they can be supported. Through a systematic review of autonomy-supportive designs within HCI research, this paper makes three contributions: a landscape overview of the existing conceptualisation of Digital Autonomy for children within HCI; a framework of 12 distinct design mechanisms for supporting children’s digital autonomy, clustered into 5 categories by their common mechanisms; and an identification of 5 critical design considerations for future support of children’s digital autonomy. Our findings provide a critical understanding of current support for children’s digital autonomy in HCI. We highlight the importance of considering children’s digital autonomy from multi-perspectives and suggest critical factors and gaps to be considered for future autonomy-supportive designs.

Information asymmetries create extractive, often harmful relationships between platform workers (e.g., Uber or Deliveroo drivers) and their algorithmic managers. Recent HCI studies have put forward more equitable platform designs but leave open questions about the social and technical infrastructures required to support them without the cooperation of platforms. We conducted a participatory design study in which platform workers deconstructed and re-imagined Uber’s schema for driver data. We analyzed the data structures and social institutions participants proposed, focusing on the stakeholders, roles, and strategies for mitigating conflicting interests of privacy, personal agency, and utility. Using critical theory, we reflected on the capability of participatory design to generate bottom-up collective data infrastructures. Based on the plurality of alternative institutions participants produced and their aptitude to navigate data stewardship decisions, we propose user-configurable tools for lightweight data institution building, as an alternative to redesigning existing platforms or delegating control to centralized trusts.

Datafication refers to the practices through which children’s online actions are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in ways including behavioural engineering and monetisation. Previous research has shown that not only do children care significantly about various aspects of datafication, but they demand a chance to take action. Through 10 co-design sessions with 53 children, we examined how children in the UK want to be supported to cope with the datafication practices. Our findings provide insights for creating age-appropriate support for children’s algorithmic literacy development, highlighting and unpacking the importance of no one-size-fitting-all designs to support children’s coping with datafication. We contribute a first understanding of how children aged 7–14 would like to be supported with datafication and what future data-driven digital experiences should be like for them, who demand a shift of the current data ecosystem towards a more humane-by-design and autonomy-supportive future.

Mobile apps used by children often make use of harmful techniques, such as data tracking and targeted advertising. Previous research has suggested that developers face several systemic challenges in designing apps that prioritise children's best interests. To understand how developers can be better supported, we used a Research through Design (RtD) method to explore what the future of privacy-friendly app development could look like. We performed an elicitation study with 20 children's app developers to understand their needs and requirements. We found a number of specific technical requirements from the participants about how they would like to be supported, such as having actionable transnational design guidelines and easy-to-use development libraries. However, participants were reluctant to adopt these design ideas in their development practices due to perceived financial risks associated with increased privacy in apps. To overcome this critical gap, participants formulated socio-technical requirements that extend to other stakeholders in the mobile industry, including parents and marketplaces. Our findings provide important immediate and long-term design opportunities for the HCI community, and indicate that support for changing app developers' practices must be designed in the context of their relationship with other stakeholders.

Critical examinations of AI systems often apply principles such as fairness, justice, accountability, and safety, which is reflected in AI regulations such as the EU AI Act. Are such principles sufficient to promote the design of systems that support human flourishing? Even if a system is in some sense fair, just, or 'safe', it can nonetheless be exploitative, coercive, inconvenient, or otherwise conflict with cultural, individual, or social values. This paper proposes a dimension of interactional ethics thus far overlooked: the ways AI systems should treat human beings. For this purpose, we explore the philosophical concept of respect: if respect is something everyone needs and deserves, shouldn't technology aim to be respectful? Despite its intuitive simplicity, respect in philosophy is a complex concept with many disparate senses. Like fairness or justice, respect can characterise how people deserve to be treated; but rather than relating primarily to the distribution of benefits or punishments, respect relates to how people regard one another, and how this translates to perception, treatment, and behaviour. We explore respect broadly across several literatures, synthesising perspectives on respect from Kantian, post-Kantian, dramaturgical, and agential realist design perspectives with a goal of drawing together a view of what respect could mean for AI. In so doing, we identify ways that respect may guide us towards more sociable artefacts that ethically and inclusively honour and recognise humans using the rich social language that we have evolved to interact with one another every day.

During the course of the past decade, children have become avid consumers of digital media through mobile devices. The industry for children's mobile applications is booming and marketplaces offer categories of apps aimed specifically at children. In this study, we perform a mixed-methods privacy analysis of 137 'expert-approved' children's apps from the Google Play Store. Our findings show that these apps do not sufficiently support children to exercise their privacy rights, whilst simultaneously making use of libraries and data trackers which may collect and share sensitive user data.

AI systems are becoming increasingly pervasive within children’s devices, apps, and services. However, it is not yet well-understood how risks and ethical considerations of AI relate to children. This paper makes three contributions to this area: first, it identifies ten areas of alignment between general AI frameworks and codes for age-appropriate design for children. Then, to understand how such principles relate to real application contexts, we conducted a landscape analysis of children’s AI systems, via a systematic literature review including 188 papers. This analysis revealed a wide assortment of applications, and that most systems’ designs addressed only a small subset of principles among those we identified. Finally, we synthesised our findings in a framework to inform a new “Code for Age-Appropriate AI”, which aims to provide timely input to emerging policies and standards, and inspire increased interactions between the AI and child-computer interaction communities.

Much effort has been invested in designing digital systems that keep people ‘hooked’. By contrast, comparatively little is known about how designers can support people in re-gaining control. Online, however, hundreds of apps and browser extensions promise to help people self-regulate use of digital devices. Reviews and popularity metrics for these digital self-control tools (DSCTs) can indicate which design patterns are useful in the wild. Moreover, they reveal how platforms like Android and iOS differ in the ecosystems they enable for DSCTs, which has important implications for end users. We analysed reviews, installation numbers, and ratings for 334 DSCTs on the Google Play, Chrome Web, and Apple App stores, investigating what user reviews reveal about usage contexts and key design challenges, and how functionality relates to popularity metrics. Our thematic analysis of 1,529 reviews (sampled from a data set of 53,978 distinct reviews scraped in March 2019) found that DSCTs are seen as highly important for focusing on less instantly rewarding tasks when digital distractions are easily available. Users seek DSCTs that adapt to their personal definitions of distraction, and provide support that is sufficient to change behaviour without feeling too coercive. Reviewers suggested combining design patterns to provide a level of support that is ‘just right’. This was mirrored in the ratings, where tools that combine different types of design patterns (e.g., website blocking and goal reminders) tended to receive higher ratings than those implementing a single type. We discuss implications for research and design, including how design patterns in DSCTs interact, and how psychological reactance to DSCTs can be reduced.

Datafication, which is the process in which children's actions online are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in multiple ways that include behavioural engineering, and monetisation, is becoming increasing common in the online world today. However, we know little about how children feel about such practices and how they perceive datafication. Through online interviews with 48 children aged 7-13 from UK schools, we examined how children perceive datafication practices, especially how such practices could make inference on them. We identified three key knowledge gaps in children's perceptions, including their lack of recognition of who were involved in the data processing and how, data being transmitted across platforms, and their data ownership. Through situating our findings under a critical algorithmic literacy framework, our findings provided some immediate indications regarding how we could better support children in the datafied society through more transparency and autonomy-supportive designs, as well as the need for a fundamental shift of the current data governance structure.

Abstract While many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. In the children’s category, iOS apps tended to use fewer advertising-related tracking than their Android counterparts, but could more often access children’s location. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law, including 1) the use of third-party tracking without user consent, 2) the lack of parental consent before sharing personally identifiable information (PII) with third-parties in children’s apps, 3) the non-data-minimising configuration of tracking libraries, 4) the sending of personal data to countries without an adequate level of data protection, and 5) the continued absence of transparency around tracking, partly due to design decisions by Apple and Google. Overall, we find that neither platform is clearly better than the other for privacy across the dimensions we studied.

Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis. Due to their relative novelty, complexity, and opacity, these technologies provoke a variety of novel questions for design and governance. We interviewed researchers, developers, industry leaders, policymakers, and designers involved in their deployment to explore motivations, expectations, perceived opportunities and barriers to adoption. This provided insight into several pertinent challenges facing the adoption of these technologies, including: how they might make a nebulous concept like privacy computationally tractable; how to make them more usable by developers; and how they could be explained and made accountable to stakeholders and wider society. We conclude with implications for the development, deployment, and responsible governance of these privacy-preserving computation techniques.

The industry for children’s apps is thriving at the cost of children’s privacy: these apps routinely disclose children’s data to multiple data trackers and ad networks. As children spend increasing time online, such exposure accumulates to long-term privacy risks. In this paper, we used a mixed-methods approach to investigate why this is happening and how developers might change their practices. We base our analysis against 5 leading data protection frameworks that set out requirements and recommendations for data collection in children’s apps. To understand developers’ perspectives and constraints, we conducted 134 surveys and 20 semi-structured interviews with popular Android children’s app developers. Our analysis revealed that developers largely respect children’s best interests; however, they have to make compromises due to limited monetisation options, perceived harmlessness of certain third-party libraries, and lack of availability of design guidelines. We identified concrete approaches and directions for future research to help overcome these barriers.

Modern conversational agents such as Alexa and Google Assistant represent significant progress in speech recognition, natural language processing, and speech synthesis. But as these agents have grown more realistic, concerns have been raised over how their social nature might unconsciously shape our interactions with them. Through a survey of 500 voice assistant users, we explore whether users' relationships with their voice assistants can be quantified using the same metrics as social, interpersonal relationships; as well as if this correlates with how much they trust their devices and the extent to which they anthropomorphise them. Using Knapp's staircase model of human relationships, we find that not only can human-device interactions be modelled in this way, but also that relationship development with voice assistants correlates with increased trust and anthropomorphism.

Parental control apps, which are mobile apps that allow parents to monitor and restrict their children's activities online, are becoming increasingly adopted by parents as a means of safeguarding their children's online safety. However, it is not clear whether these apps are always beneficial or effective in what they aim to do; for instance, the overuse of restriction and surveillance has been found to undermine parent-child relationship and children's sense of autonomy. While previous research has categorised and taken inventory of key features of popular parental control apps, they have not systematically analysed the ways such features were designed or realised in such apps, or in particular how aspects of such designs might relate to parents and children's experiences with such apps. In this work, we investigate this gap, asking specifically: how might children's and parents' perceptions be related to how parental control features were designed? To investigate this question, we conducted an analysis of 58 top Android parental control apps designed for the purpose of promoting children's online safety, finding three major axes of variation in how key restriction and monitoring features were realised: granularity, feedback/transparency, and parent-child communications support. To relate these axes to perceived benefits and problems, we then analysed 3264 app reviews to identify references to aspects of the each of the axes above, to understand children's and parents' views of how such dimensions related to their experiences with these apps. Our findings led towards 1) an understanding of how parental control apps realise their functionalities differently along three axes of variation, 2) an analysis of exactly the ways that such variation influences children's and parents' perceptions, respectively of the usefulness or effectiveness of these apps, and finally 3) an identification of design recommendations and opportunities for future apps by contextualising our findings within existing digital parenting theories.

The increasingly widespread use of 'smart' devices has raised multifarious ethical concerns regarding their use in domestic spaces. Previous work examining such ethical dimensions has typically either involved empirical studies of concerns raised by specific devices and use contexts, or alternatively expounded on abstract concepts like autonomy, privacy or trust in relation to 'smart homes' in general. This paper attempts to bridge these approaches by asking what features of smart devices users consider as rendering them 'smart' and how these relate to ethical concerns. Through a multimethod investigation including surveys with smart device users (n=120) and semi-structured interviews (n=15), we identify and describe eight types of smartness and explore how they engender a variety of ethical concerns including privacy, autonomy, and disruption of the social order. We argue that this middle ground, between concerns arising from particular devices and more abstract ethical concepts, can better anticipate potential ethical concerns regarding smart devices.

Beyond being the world's largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of 'distraction'.

Connected devices in the home represent a potentially grave new privacy threat due to their unfettered access to the most personal spaces in people's lives. Prior work has shown that despite concerns about such devices, people often lack sufficient awareness, understanding, or means of taking effective action. To explore the potential for new tools that support such needs directly we developed Aretha, a privacy assistant technology probe that combines a network disaggregator, personal tutor, and firewall, to empower end-users with both the knowledge and mechanisms to control disclosures from their homes. We deployed Aretha in three households over six weeks, with the aim of understanding how this combination of capabilities might enable users to gain awareness of data disclosures by their devices, form educated privacy preferences, and to block unwanted data flows. The probe, with its novel affordances-and its limitations-prompted users to co-adapt, finding new control mechanisms and suggesting new approaches to address the challenge of regaining privacy in the connected home.

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 from UK schools, we examined how children described privacy risks related to their use of tablet computers and what information was used by them to identify threats. We found that children could identify and articulate certain privacy risks well, such as information oversharing or revealing real identities online; however, they had less awareness with respect to other risks, such as online tracking or game promotions. Our findings offer promising directions for supporting children's awareness of cyber risks and the ability to protect themselves online.

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

Most smartphone apps collect and share information with various first and third parties; yet, such data collection practices remain largely unbeknownst to, and outside the control of, end-users. In this paper, we seek to understand the potential for tools to help people refine their exposure to third parties, resulting from their app usage. We designed an interactive, focus-plus-context display called X-Ray Refine (Refine) that uses models of over 1 million Android apps to visualise a person's exposure profile based on their durations of app use. To support exploration of mitigation strategies, emphRefine can simulate actions such as app usage reduction, removal, and substitution. A lab study of emphRefine found participants achieved a high-level understanding of their exposure, and identified data collection behaviours that violated both their expectations and privacy preferences. Participants also devised bespoke strategies to achieve privacy goals, identifying the key barriers to achieving them.

Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a two-part investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.

Third-party networks collect vast amounts of data about users via websites and mobile applications. Consolidations among tracker companies can significantly increase their individual tracking capabilities, prompting scrutiny by competition regulators. Traditional measures of market share, based on revenue or sales, fail to represent the tracking capability of a tracker, especially if it spans both web and mobile. This article proposes a new approach to measure the concentration of tracking capability, based on the reach of a tracker on popular websites and apps. Our results reveal that tracker prominence and parent–subsidiary relationships have significant impact on accurately measuring concentration.

Most users of smartphone apps remain unaware of what data about them is being collected, by whom, and how these data are being used. In this mixed methods investigation, we examine the question of whether revealing key data collection practices of smartphone apps may help people make more informed privacy-related decisions. To investigate this question, we designed and prototyped a new class of privacy indicators, called Data Controller Indicators (DCIs), that expose previously hidden information flows out of the apps. Our lab study of DCIs suggests that such indicators do support people in making more confident and consistent choices, informed by a more diverse range of factors, including the number and nature of third-party companies that access users' data. Furthermore, personalised DCIs, which are contextualised against the other apps an individual already uses, enable them to reason effectively about the differential impacts on their overall information exposure.

Online citizen science projects have been increasingly used in a variety of disciplines and contexts to enable large-scale scientific research. The successes of such projects have encouraged the development of customisable platforms to enable anyone to run their own citizen science project. However, the process of designing and building a citizen science project remains complex, with projects requiring both human computation and social aspects to sustain user motivation and achieve project goals. In this paper, we conduct a systematic survey of 48 citizen science projects to identify common features and functionality. Supported by online community literature, we use structured walkthroughs to identify different mechanisms used to encourage volunteer contributions across four dimensions: task visibility, goals, feedback, and rewards. Our findings contribute to the ongoing discussion on citizen science design and the relationship between community and microtask design for achieving successful outcomes.

Deception is typically regarded as a morally impoverished choice. However, in the context of increasingly intimate, connected and ramified systems of online interaction, manipulating information in ways that could be considered deceptive is often necessary, useful, and even morally justifiable. In this study, we apply a speculative design approach to explore the idea of tools that assist in pro-social forms of online deception, such as those that conceal, distort, falsify and omit information in ways that promote sociality. In one-on-one semi-structured interviews, we asked 15 participants to respond to a selection of speculations, consisting of imagined tools that reify particular approaches to deception. Participants reflected upon potential practical, ethical, and social implications of the use of such tools, revealing a variety of ways such tools might one day encourage polite behaviour, support individual autonomy, provide a defence against privacy intrusions, navigate social status asymmetries, and even promote more open, honest behaviour.

While the Quantified Self and personal informatics fields have focused on the individual's use of self-logged data about themselves, the same kinds of data could, in theory, be used to improve diagnosis and care planning. In this paper, we seek to understand both the opportunities and bottlenecks in the use of self-logged data for differential diagnosis and care planning during patient visits to both primary and secondary care. We first conducted a literature review to identify potential factors influencing the use of self-logged data in clinical settings. This informed the design of our experiment, in which we applied a vignette-based role-play approach with general practitioners and hospital specialists in the US and UK, to elicit reflections on and insights about using patient self-logged data. Our analysis reveals multiple opportunities for the use of self-logged data in the differential diagnosis workflow, identifying capture, representational, and interpretational challenges that are potentially preventing self-logged data from being effectively interpreted and applied by clinicians to derive a patient's prognosis and plan of care.

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable. Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself. We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform. Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades. In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features. We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; e.g., when a cascade branches into multiple, divergent cascades with combined capacity equal to the original.

Designing an effective and sustainable citizen science (CS)project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement, supported by two-to-four specific design claims each. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.

Crowdsourcing via paid microtasks has been successfully applied in a plethora of domains and tasks. Previous efforts for making such crowdsourcing more effective have considered aspects as diverse as task and workflow design, spam detection, quality control, and pricing models. Our work expands upon such efforts by examining the potential of adding gamification to microtask interfaces as a means of improving both worker engagement and effectiveness. We run a series of experiments in image labeling, one of the most common use cases for microtask crowdsourcing, and analyse worker behavior in terms of number of images completed, quality of annotations compared against a gold standard, and response to financial and game-specific rewards. Each experiment studies these parameters in two settings: one based on a state-of-the-art, non-gamified task on CrowdFlower and another one using an alternative interface incorporating several game elements. Our findings show that gamification leads to better accuracy and lower costs than conventional approaches that use only monetary incentives. In addition, it seems to make paid microtask work more rewarding and engaging, especially when sociality features are introduced. Following these initial insights, we define a predictive model for estimating the most appropriate incentives for individual workers, based on their previous contributions. This allows us to build a personalised game experience, with gains seen on the volume and quality of work completed.

The information processing capabilities of humans enable them to opportunistically draw and integrate knowledge from nearly any information source. However, the integration of digital, structured data from diverse sources remains difficult, due to problems of heterogeneity that arise when data modelled separately are brought together. In this paper, we present an investigation of the feasibility of extending Personal Information Management (PIM) tools to support lightweight, user-driven mixing of previously un-integrated data, with the objective of allowing users to take advantage of the emerging ecosystems of structured data currently becoming available. In this study, we conducted an exploratory, sequential, mixed-method investigation, starting with two pre-studies of the data integration needs and challenges, respectively, of Web-based data sources. Observations from these pre-studies led to DataPalette, an interface that introduced simple co-reference and group multi-path-selection mechanisms for working with terminologically and structurally heterogeneous data. Our lab study showed that participants readily understood the new interaction mechanisms which were introduced. Participants made more carefully justified decisions, even while weighing a greater number of factors, moreover expending less effort, during subjective-choice tasks when using DataPalette, than with a control set-up.

Millions of recommendations, opinions and experiences are shared across popular microblogging platforms and services each day. Yet much of this content becomes quickly lost in the stream shortly after being posted. This paper looks at the feasibility of identifying useful content in microblog streams so that it might be archived to facilitate wider access and reference. Towards this goal, we present an experiment with a game-with-a-purpose called Twiage that we designed to determine how well the deluge of content in "raw" microblog streams could be turned into filtered and ranked collections using ratings from players. Experiments with Twiage validate the feasibility of applying human-computation to this problem, finding strong agreement about what constitutes the "most useful" content in our test dataset. Second, we compare the effectiveness of various methods of eliciting such ratings, finding that a "choose-best" interface and Elo rating ranking scheme yield the greatest agreement in the fewest rounds. External validation of resulting top-rated twitter content with a domain expert found that while the top Twiage-ranked "tweets" were among the best of the set, there was a tendency for players to also select what we term "weak spam" - e.g., promotional content disguised as articles or reviews, indicating a need for more stringent content filtering.

Mainstream PIM tools capture only a portion of the information that people need to manage. Many information scraps seem to exist that don't make their way into these tools, instead being relegated to sticky notes, text files, and other makeshift storage, or simply being lost. In an effort to understand the role of these information scraps, the underlying needs they reflect, and the way PIM tools must be modified to support those needs, we created List-it, a micronote tool for quick and simple capture of information scraps.

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this paper, we describe a next-generation personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their timevarying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate's open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model.

This paper describes a longitudinal field experiment in personal note-taking that examines how people capture and use information in short textual notes. Study participants used our tool, a simple browser-based textual note-taking utility, to capture personal information over the course of ten days. We examined the information they kept in notes using the tool, how this information was expressed, and aspects of note creation, editing, deletion, and search. We found that notes were recorded extremely quickly and tersely, combined information of multiple types, and were rarely revised or deleted. The results of the study demonstrate the need for a tool such as ours to support the rapid capture and retrieval of short notes-to-self, and afford insights into how users' actual note-keeping tendencies could be used to better support their needs in future PIM tools.

Auditory contexts are recognized from mixtures of sounds from mobile userspsila everyday environments. We describe our implementation of auditory context recognition for mobile devices. In our system we use a set of support vector machine classifiers to implement the recognizer. Moreover, static and runtime resource consumption of the system are measured and reported.

Recent advances in peer-to-peer (P2P) technologies will enable users to establish ubiquitous connectivity among their personal networked devices and those of others. Building on top of such technologies, we propose a platform of middleware and user interaction tools, called MyNet, that allows everyday users to easily and securely access and share with others their devices, services, and content, without requiring expertise or centralized service support. MyNet offers a more immediate and responsive alternative to the current web-based paradigm of personal and social networking, because it allows users’ distributed services and content to be accessed and shared in real-time as they are produced, directly from their personal devices. In this paper, we describe the MyNet system architecture, including secure resource discovery, service management, security framework, and the user interaction tools for building personal and social networks and sharing resources over them. We also present our proof-of-concept implementation, including mobile devices and our tests with real users.

We present Inky, a command line for shortcut access to common web tasks.Inky aims to capture the efficiency benefits of typed commands while mitigating their usability problems.Inky commands have little or no new syntax to learn, and the system displays rich visual feedback while the user is typing, including missing parameters and contextual information automatically clipped from the target web site.Inky is an example of a new kind of hybrid between a command line and a GUI interface.We describe the design and implementation of two prototypes of this idea, and report the results of a field study.

In this article we investigateinformation scraps—personal information where content has been scribbled on Post-it notes, scrawled on the corners of sheets of paper, stuck in our pockets, sent in email messages to ourselves, and stashed in miscellaneous digital text files. Information scraps encode information ranging from ideas and sketches to notes, reminders, shipment tracking numbers, driving directions, and even poetry. Although information scraps are ubiquitous, we have much still to learn about these loose forms of information practice. Why do we keep information scraps outside of our traditional PIM applications? What role do information scraps play in our overall information practice? How might PIM applications be better designed to accommodate and support information scraps' creation, manipulation and retrieval? We pursued these questions by studying the information scrap practices of 27 knowledge workers at five organizations. Our observations shed light on information scraps' content, form, media, and location. From this data, we elaborate on the typical information scrap lifecycle, and identify common roles that information scraps play: temporary storage, archiving, work-in-progress, reminding, and management of unusual data. These roles suggest a set of unmet design needs in current PIM tools: lightweight entry, unconstrained content, flexible use and adaptability, visibility, and mobility.

Information cannot be found if it is not recorded. Existing rich graphical application approaches interfere with user input in many ways, forcing complex interactions to enter simple information, requiring complex cognition to decide where the data should be stored, and limiting the kind of information that can be entered to what can fit into specific applications' data models. Freeform text entry suffers from none of these limitations but produces data that is hard to retrieve or visualize. We describe the design and implementation of Jourknow, a system that aims to bridge these two modalities, supporting lightweight text entry and weightless context capture that produces enough structure to support rich interactive presentation and retrieval of the arbitrary information entered.

A vision-based virtual mouse interface is described that utilizes a robotic head, visual tracking of the users head and hand positions and recognition of user hand signs to control an intelligent kiosk. The user interface supports, among other things, smooth control of the mouse pointer and buttons using hand signs and movements. The algorithms and architecture of real-time vision and robot controller are described.

We explored the use of awareness information to facilitate communication by developing a series of prototypes. The ConNexus prototype integrates awareness information, instant messaging, and other communication channels in an interface that runs on a desktop computer. The Awarenex prototype extends that functionality to wireless handheld devices, such as a Palm. A speech interface also enables callers to make use of the awareness information over the telephone. While the prototypes offer similar functionality, the interfaces reflect the different design affordances and use context of each platform. We discuss the design implications of providing awareness information on devices with varying interface and network characteristics.